SAR Despeckling via Log-Yeo-Johnson Transformation and Sparse Representation

TL;DR

This paper introduces a novel SAR despeckling method that uses the Log-Yeo-Johnson transformation to convert speckle noise into a Gaussian form, enabling effective sparse representation and noise reduction.

Contribution

It presents an innovative despeckling approach combining the Log-Yeo-Johnson transform with non-local sparse representation based on compressive sensing theory.

Findings

Effective noise reduction demonstrated in SAR images

Improved image quality over traditional methods

Adaptive sparsity modeling enhances despeckling

Abstract

Synthetic Aperture Radar (SAR) images are widely used in remote sensing due to their all-weather, all-day imaging capabilities. However, SAR images are highly susceptible to noise, particularly speckle noise, caused by the coherent imaging process, which severely degrades image quality. This has driven increasing research interest in SAR despeckling. Sparse representation-based denoising has been extensively applied in natural image processing, yet SAR despeckling requires addressing non-Gaussian noise and ensuring sparsity in the transform domain. In this work, we propose an innovative SAR despeckling approach grounded in compressive sensing theory. By applying the Log-Yeo-Johnson transformation, we convert gamma-distributed noise into an approximate Gaussian distribution, facilitating sparse representation. The method incorporates noise and sparsity priors, leveraging a non-local sparse representation through auxiliary matrices: one capturing varying noise characteristics across regions and the other encoding adaptive sparsity information.